socks.cstc.4.2.tar

说明：  利用Bp神经网络实现的两类分类程序，程序简单明了，初学者也看得懂。(Bp neural network using two types of classification procedures, the program is simple, beginners can understand.)

用PHP 和mySQL 搭建的图书馆管理系统，实现图书查询，入库，借书还书等功能-with PHP and mySQL built library management system, library inquiries, storage, library book and other functions

Python 机器学习与实战 第三章 决策树分类问题(Python Machine Learning and practical decision tree classification Chapter III)

c写的生成er图边集，很简单实用，em...............................................................................推荐大家使用..............................................................................................

基于改进的遗传算法的城市交通信号优化分析，以车辆平均延误时间最短为目标，可直接运行(Based on the improved genetic algorithm for urban traffic signal optimization analysis, the average delay time of the vehicle is the shortest target, which can be run directly)

可对本机网络访问信息进行监控 解压后运行HSSnifferSeniorProjectSnifferUIinDebugsnifferUI.exe即可，enjoy it！-be right for the network access control information unpacked running HSSniffer SeniorProject SnifferUI bin Debug snifferUI.exe can, enjoy it!

无导师学习神经网络的分类——矿井突水水源判别(Unsupervised learning neural network classification- mine water inrush discrimination)

该MDL主要通过Matlab Simulink实现神经网络的PID控制，两个例子中神经网络模块分别利用SimulinkUser-Defined FunctionsMATLAB Fcn和S函数方式实现。在S函数中引入T,nh,xite,alfa,kf1,kf2等参数，在S-Function模块的Parameters处予以初始化。并附带了S函数的使用方法，供大家学习Simulink中两种自定义模块的使用方法。(The MDL mainly through the Matlab Simulink neural network PID control, two examples, neural network module using SimulinkUser-Defined, FunctionsMATLAB, Fcn and S function way to achieve. In the S function, T, NH, xite, Alfa, KF1, KF2, and other parameters are introduced and initialized at the Parameters of the S-Function module. And accompanied by the use of S function, for everyone to learn Simulink two custom modules in the use of methods.)

ASP网页，里面有详细的说明，很有用的，希望大家喜欢-ASP page, which has detailed instructions, very useful, I hope everyone likes

二维经验模态分解，采用形态学求极值点，径向基神经网络拟合，速度较快。(Two-dimensional empirical mode decomposition, using morphology for extreme points, radial basis function neural network fitting, faster.)